This project presents an in-depth sales analysis of a fictional electronics retailer using Python, Pandas, and Matplotlib. The goal is to uncover key business insights such as peak sales months, best-selling products, and city-wise performance to guide strategic decision-making.
- Identify the best month for sales.
- Determine which city had the highest number of sales.
- Analyze peak hours for advertisements.
- Discover products most often sold together.
- Study the relationship between product price and quantity sold.
- Data: Monthly sales data in CSV format.
- Source: Provided for the purpose of this analysis.
- Python
- Pandas
- Matplotlib
- Jupyter Notebook
- 📈 December was the highest-grossing month, generating the highest overall revenue.
- 🌆 San Francisco led in product sales volume.
- ⏰ Customer purchase activity peaked around 11 AM and 7 PM.
- 🔌 iPhone and Lightning Charging Cable were frequently purchased together.
- 💸 Lower-priced items sold in higher volumes compared to premium products.